optical-flow | optical flow - More details see here http | Code Inspection library

cv::NORM_L2 normalises your data such that if you took the entire image and converted it into one long vector, the magnitude of this vector is such that it becomes alpha. beta is ignored in the normalisation. Therefore, once you normalise by cv::NORM_L2, after you normalise if you were to consider this normalised input as one long vector, the L₂ norm of this vector thus becomes alpha. Because you specified alpha = 0, it's not surprising that the output image is entirely 0 because you are specifying that the norm needs to be 0 after normalisation.

cv::NORM_MINMAX uses both alpha and beta such that the smallest value in the input array gets mapped to alpha and the largest value gets mapped to beta with all values in between scaled proportionally.

If you're wondering how I know this, the documentation for the function makes this very clear: https://docs.opencv.org/4.3.0/d2/de8/group__core__array.html#ga87eef7ee3970f86906d69a92cbf064bd

For cv2 version "'4.1.0'":

Code below is faster but is less accurate as per the explanation of hyperparameters below. Tune these parameters to solve speed vs accuracy trade-off as per requirement.

I found the solution here.

The way to call the function is different with the latest openCV version. Here is what to do :

Replace